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This research developed an adaptive control system for injection molding process. The purpose of this control system is to adaptively maintain the consistency of product quality by minimize the mass variation of injection molded parts. The adaptive control system works with the information collected through two sensors installed in the machine only—the injection nozzle pressure sensor and the temperature sensor. In this research, preliminary experiments are purposed to find master pressure curve that relates to product quality. Viscosity index, peak pressure, and timing of the peak pressure are used to characterize the pressure curve. The correlation between product quality and parameters such as switchover position and injection speed were used to produce a training data for back propagation neural network (BPNN) to compute weight and bias which are applied on the adaptive control system. By using this system, the variation of part weight is maintained to be as low as 0.14%.

The absolute longitudinal distance between two points can be deterinined by the corresponding correlation peaks of two light-waves from a broad-band light source. Using this technique, the height of three-dimensional objects can be measured without 2lz: phase ambiguity. We can also detect the absolute position of scattering seeds in sub-surface or bulk materials such as defects, dislocations or impurities of high purity materials. The wavelet analysis is used to determine the correlation peaks. This technique can be applied to measurement of thickness of a few hundred microns.

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